At Afresh, I was a Founding Engineer (now ~200 employees, ~$15M ARR).
I was a key architect building our Machine Learning, Data, Backend, Product, SRE/Infrastructure, and DevOps systems and platforms from scratch. 80+ engineers now build upon the foundations I created and the systems I designed.
Outside of the code, I shaped the company’s culture, processes, and priorities that continue to influence the company to this day.
Machine Learning
- Wrote ML pipelines for demand forecasting, inventory estimation, and ordering policy that comprise the recommendation engine (Python, Pandas, XGBoost, TensorFlow).
- Designed modularized ML architecture that underpins our entire ML system.
- Integrated ML infrastructure for serverless GPU computing, automated profiling, and performance monitoring.
Data
- Built and managed customer data integrations, ETLs, data ingestion, and automated pipelines (Python, Pandas, Airflow) for all enterprise partners.
- Designed our data models and schemas for our relational DB and data warehouse (Postgres, Snowflake).
- Built DB APIs and data platforms that serve all data operations (Python, TypeScript, Sequelize, SQLAlchemy).
Backend
- Architected our codebases (ML, Data, and Product backends), development environments (TypeScript, Python), and testing environments (integrated and unit).
- Built backend server (Node.js, TypeScript) and designed API endpoints (GraphQL) that power clients for all product features.
- Designed and built context management, error handling, and logging for both our Python and TypeScript repositories.
- Implemented type systems, extensive linting and auto-formatters, and dependency management for both our Python and TypeScript repositories.
Product
- Designed and implemented data models, data loaders, and APIs powering all client functionality, including orders and inventory.
- Designed and built the fundamental key features end-to-end, from ETL to ML to DB to APIs to GraphQL.
- Implemented fundamental backend functionality such as authentication, user and state management, and auto-scaling (Terraform).
SRE / Infrastructure
- Implemented CI/CD workflows for builds, testing, linting, and Kubernetes deployments (CircleCI, AWS, Azure) across multiple services and backends.
- Created internal processes for deployments, incident response, project planning, code review, and testing.
- Integrated third-party services for monitoring and alerting across all services (Datadog, Sentry, PagerDuty).
The following shows the number of pull requests merged within an arbitrary six-month period while building Afresh. (This is only for six months, a fraction of my tenure.)
